7DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Status of the Claims
The pending claims in the present application are original claims 1-20 of 14 December 2022.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 14 December 2022 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The paragraphs below provide rationales for the rejection. The rationales are based on the multi-step subject matter eligibility test outlined in MPEP 2106.
Step 1 of the eligibility analysis involves determining whether a claim falls within one of the four enumerated categories of patentable subject matter recited in 35 USC 101. (See MPEP 2106.03(I).) That is, Step 1 asks whether a claim is to a process, machine, manufacture, or composition of matter. (See MPEP 2106.03(II).) The “apparatus” of claims 1-8 constitutes a machine under 35 USC 101, the “method” of claims 9-13 constitutes a process under the statute, and the “non-transitory computer readable medium” of claims 14-20 constitutes a manufacture under the statute. Accordingly, claims 1-20 meet the criteria of Step 1 of the eligibility analysis. The claims, however, fail to meet the criteria of subsequent steps of the eligibility analysis, as explained in the paragraphs below.
The next step of the eligibility analysis, Step 2A, involves determining whether a claim is directed to a judicial exception. (See MPEP 2106.04(II).) This step asks whether a claim is directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea. (See id.) Step 2A is a two-prong inquiry. (See MPEP 2106.04(II)(A).) Prong One and Prong Two are addressed below.
In the context of Step 2A of the eligibility analysis, Prong One asks whether a claim recites an abstract idea, law of nature, or natural phenomenon. (See MPEP 2106.04(II)(A)(1).) Using claim 1 as an example, the claim recites the following abstract idea limitations:
“An energy efficient collaboration for environmental social and governance (ESG) data consolidation and validation ... comprising: ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... a correlation graph generator ... to: determine local correlations between ESG dimensions by determining correlation between historical data sets for corresponding ESG dimensions; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... generate, based on the determined local correlations ... of a plurality ..., a local correlation graph of associated ESG dimensions; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... determine, based on the local correlation graph of associated ESG dimensions, global correlations between the ESG dimensions by determining mean correlation between specified ESG dimensions across the plurality ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... generate, based on the determined global correlations and for each ... of the plurality ..., a global correlation graph of associated ESG dimensions; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... an ESG dimension analyzer ... to: identify, based on the global correlation graph, correlated ESG dimensions for each ESG data analyzer of a plurality of ESG data analyzers with respect to a set of ESG dimensions on which an ESG data analyzer of the plurality of ESG data analyzers collects data for at least one ... of the plurality ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... a collaboration analyzer ... to: determine, based on the correlated ESG dimensions for each ESG data analyzer of the plurality of ESG data analyzers, collaboration potential between the plurality of ESG data analyzers; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... a collaboration potential analyzer ... to: generate, based on the collaboration potential between the plurality of ESG data analyzers, decentralized groups of collaborating ESG data analyzers; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... a data model generator ... to: update, for each decentralized group of the decentralized groups of collaborating ESG data analyzers, a data model for each ESG data analyzer with respect to each ESG dimension corresponding to each OAE for which the ESG data analyzer is collecting data; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... an anomalous event analyzer ... to: identify, for the ESG data analyzer that is collecting data and based on an associated updated data model, a potential anomalous ESG event at a specific ESG dimension; and ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... a ... controller ... to: control, based on the identified potential anomalous ESG event, operation of the OAE associated with the ESG data analyzer that is collecting data. ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
The above-listed limitations of claim 1, when applying their broadest reasonable interpretations in light of their context in the claim as a whole, fall under enumerated groupings of abstract ideas outlined in MPEP 2106.04(a). For example, limitations of the claim can be characterized as: commercial or legal interactions, including interactions involving legal obligations associated with environmental, social, and governance (ESG); and managing personal behavior or relationships or interactions between people, including collaboration among analyzers of ESG data, which fall under the certain methods of organizing human activity grouping of abstract ideas (see MPEP 2106.04(a)). Limitations of the claim also can be characterized as: concepts performed in the human mind, including evaluation, judgment, and/or opinion (e.g., the recited “determine,” “generate,” “identify,” “update,” and “control” steps), which fall under the mental processes grouping of abstract ideas (see MPEP 2106.04(a)). Accordingly, for at least these reasons, claim 1 fails to meet the criteria of Step 2A, Prong One of the eligibility analysis.
In the context of Step 2A of the eligibility analysis, Prong Two asks if the claim recites additional elements that integrate the judicial exception into a practical application. (See MPEP 2106.04(II)(A)(2).) Continuing to use claim 1 as an example, the claim recites the following additional element limitations:
The claimed “energy efficient collaboration for environmental social and governance (ESG) data consolidation and validation” involves use of an “apparatus” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
Use of “at least one hardware processor” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “correlation graph generator” is “executed by the at least one hardware processor” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “generate” is “for each organization avatar entity (OAE)” that is of a “plurality of OAEs” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “ESG dimension analyzer” is “executed by the at least one hardware processor” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “collaboration analyzer” is “executed by the at least one hardware processor” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “collaboration potential analyzer” is “executed by the at least one hardware processor” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “data model generator” is “executed by the at least one hardware processor” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “anomalous event analyzer” is “executed by the at least one hardware processor” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “controller” is for the “OAE” and is “executed by the at least one hardware processor” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The above-listed additional element limitations of claim 1, when applying their broadest reasonable interpretations in light of their context in the claim as a whole, are analogous to: accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer, and mere automation of manual processes, which courts have indicated may not be sufficient to show an improvement in computer-functionality (see MPEP 2106.05(a)(I)); a commonplace business method being applied on a general purpose computer, gathering and analyzing information using conventional techniques and displaying the result, and selecting a particular generic function for computer hardware to perform from within a range of fundamental or commonplace functions performed by the hardware, which courts have indicated may not be sufficient to show an improvement to technology (see MPEP 2106.05(a)(II)); a general purpose computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions, and merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions, which do not qualify as a particular machine or use thereof (see MPEP 2106.05(b)(I)); a machine that is merely an object on which the method operates, which does not integrate the exception into a practical application (see MPEP 2106.05(b)(II)); use of a machine that contributes only nominally or insignificantly to the execution of the claimed method, which does not integrate a judicial exception (see MPEP 2106.05(b)(III)); transformation of an intangible concept such as a contractual obligation or mental judgment, which is not likely to provide significantly more (see MPEP 2106.05(c)); use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea, a commonplace business method or mathematical algorithm being applied on a general purpose computer, and requiring the use of software to tailor information and provide it to the user on a generic computer, which courts have found to be mere instructions to apply an exception, because they do no more than merely invoke computers or machinery as a tool to perform an existing process (see MPEP 2106.05(f)); mere data gathering in the form of obtaining information about transactions using the Internet to verify transactions and consulting and updating an activity log, which courts have found to be insignificant extra-solution activity (see MPEP 2106.05(g)); and specifying that the abstract idea of monitoring audit log data relates to transactions or activities that are executed in a computer environment, because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer, which courts have described as merely indicating a field of use or technological environment in which to apply a judicial exception (see MPEP 2106.05(h)). For at least these reasons, claim 1 fails to meet the criteria of Step 2A, Prong Two of the eligibility analysis.
The next step of the eligibility analysis, Step 2B, asks whether a claim recites additional elements that amount to significantly more than the judicial exception. (See MPEP 2106.05(II).) The step involves identifying whether there are any additional elements in the claim beyond the judicial exceptions, and evaluating those additional elements individually and in combination to determine whether they contribute an inventive concept. (See id.) The ineligibility rationales applied at Step 2A, Prong Two, also apply to Step 2B. (See id.) For all of the reasons covered in the analysis performed at Step 2A, Prong Two, claim 1 fails to meet the criteria of Step 2B. As a result, claim 1 is rejected under 35 USC 101 as ineligible for patenting.
Regarding claims 2-8, the claims depend from claim 1, and expand upon limitations introduced by claim 1. The dependent claims are rejected at least for the same reasons as claim 1. For example, the dependent claims recite abstract idea elements similar to the abstract idea elements of claim 1, that fall under the same abstract idea groupings as the abstract idea elements of claim 1 (e.g., the “wherein the collaboration analyzer is executed ... to determine, based on the correlated ESG dimensions for each ESG data analyzer of the plurality of ESG data analyzers, the collaboration potential between the plurality of ESG data analyzers by: determining, for each pair of ESG data analyzers of the plurality of ESG data analyzers, a degree to which ESG data analyzers of the pair of ESG data analyzers collaborate with each other to enrich their data collection” of claim 2, the “wherein the collaboration potential analyzer is executed ... to generate, based on the collaboration potential between the plurality of ESG data analyzers, the decentralized groups of collaborating ESG data analyzers by: generating a collaboration graph between the plurality of ESG data analyzers, wherein, for the collaboration graph, weights of edges represent collaboration potentials between connected ESG data analyzers” of claim 3, the “wherein the collaboration potential analyzer is executed ... to generate the collaboration graph between the plurality of ESG data analyzers by: retaining, for the collaboration graph, edges that include a collaboration potential that is greater than a specified threshold” of claim 4, the “wherein the anomalous event analyzer is executed ... to identify, for the ESG data analyzer that is collecting data and based on the associated updated data model, the potential anomalous ESG event at the specific ESG dimension by: rebuilding, for the ESG data analyzer that is collecting data, a local data model to generate the updated data model; and determining a difference between the updated data model from the local data model” of claim 5, the “wherein the anomalous event analyzer is executed ... to: determine, for a specified number of ESG dimensions, whether a data anomaly is true for the ... associated with the ESG data analyzer; and identify, based on a determination that the data anomaly is true for the ... associated with the ESG data analyzer for the specified number of ESG dimensions, the potential anomalous ESG event” of claim 6, the “wherein the anomalous event analyzer is executed ... to: determine, for a specified number ... in a cluster ..., whether a data anomaly is true; and identify, based on a determination that the data anomaly is true for the specified number ... in the cluster ..., the potential anomalous ESG event for the cluster” of claim 7, and the “wherein the anomalous event analyzer is executed ... to: determine, for a specified number ... in a cluster ..., whether a data anomaly is true for a specified ESG dimension; and identify, based on a determination that the data anomaly is true for the specified ESG dimension for the specified number ... in the cluster ..., the potential anomalous ESG event at a global level for the specified ESG dimension” of claim 8). The dependent claims recite further additional elements that are similar to the additional elements of claim 1, that fail to warrant eligibility for the same reasons as the additional elements of claim 1 (e.g., the “apparatus ... by the at least one hardware processor” of claims 2-8, the “OAE” of claim 6, and the “OAEs” of claims 7 and 8). Accordingly, claims 2-8 also are rejected as ineligible under 35 USC 101.
Regarding claims 9-13, while the claims are of different scope relative to claims 1, 3, and 4, the claims recite limitations similar to the limitations of claims 1, 3, and 4. As such, the rejection rationales applied to reject claims 1, 3, and 4 also apply for purposes of rejecting claims 9-13. Claims 9-13 are, therefore, also rejected as ineligible under 35 USC 101.
Regarding claims 14-20, while the claims are of different scope relative to claims 1 and 5-9, the claims recite limitations similar to the limitations of claims 1 and 5-9. As such, the rejection rationales applied to reject claims 1 and 5-9 also apply for purposes of rejecting claims 14-20. Claims 14-20 are, therefore, also rejected as ineligible under 35 USC 101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pat. App. Pub. No. 2024/0086817 A1 to Bessenyei et al. (hereinafter referred to as “Bessenyei”), in view of WIPO Int’l Pub. No. 2020/259716 A1 to Li et al. (hereinafter referred to as “Li”), and further in view of Berg F, Kölbel JF, Rigobon R. Aggregate confusion: The divergence of ESG ratings. Review of finance. 2022 Nov 1;26(6):1315-44 (hereinafter referred to as “Berg”).
Regarding claim 1, the combination of Bessenyei, Li, and Berg (hereinafter referred to as “Bessenyei/Li/Berg”) teaches limitations below that do not appear to be disclosed in their entirety by the references individually:
“An energy efficient collaboration for environmental social and governance (ESG) data consolidation and validation apparatus comprising: ...” - Bessenyei discloses, “A Software-as-a-Service infrastructure is provided to perform data analytics, wherein a given analytic is defined in a spreadsheet according to a formula, and wherein the formula is associated with an Environmental, Social and Governance (ESG) impact” (Abstract), “Participating end users 104” (para. [0020]), “The imputation stage 112 receives the output(s) from the preprocessing stage 110 and operates to fill-in data that is not present in the one or more data sources directly but may still be needed for computations” (para. [0025]), and “The flowchart in FIG. 2 represents an operation of the aggregator stage 116. In general, this stage configures the computation engine, executes the computation(s) in a spreadsheet, aggregates data resulting from such computation(s)” (para. [0026]). The SaaS infrastructure for multiple end users for ESG impact, including aggregating data and filling in missing data, in Bessenyei, reads on the recited limitation.
“... at least one hardware processor; ...” - Bessenyei discloses, “a set of hardware processors” in its claim 1.
“... a correlation graph generator, executed by the at least one hardware processor, to: ...” - Bessenyei discloses, “computer program code executed by the one or more hardware processors” in its claim 1. Computer program code executed by the one or more hardware processors, in Bessenyei, reads on the recited “executed by the at least one hardware processor, to” limitation. Bessenyei does not appear to disclose generating a correlation graphs. Li, on the other hand, discloses “processing the alternative data to determine a knowledge graph” (English-language translation, p. 2), and “FIG. 3 is a detailed flowchart of step S100 of an embodiment of the ESG indicator monitoring method of this application. In this embodiment, the knowledge graph includes: a key information graph. In step S100, the alternative The steps of data processing to determine the knowledge graph include: Step S101, extracting key information from the alternative data based on artificial intelligence technology, where the key information includes: key nodes and key relationships” (English-language translation, p. 4). The knowledge graph and key information graph generator, in Li, reads on the recited “correlation graph generator” limitation.
“... determine local correlations between ESG dimensions by determining correlation between historical data sets for corresponding ESG dimensions; ...” - Bessenyei discloses, “Depending on use case, the data sources may be public data, e.g., published or otherwise readily-accessible data about public companies, private data, e.g., data about a private company and that is not necessarily published or otherwise available from public sources, or a combination of both public and private data. Whether public or private, typically the data is associated with one or ESG-related metrics associated with a company or a group of companies” (para. [0021]), and “the data sources 106 comprise a taxonomy of product groups and associated key impact drivers available to the platform” and “outcome-driven environmental and social metrics (e.g., product portfolio, grams of sugar per $ of revenue, grams of plastic per $ of revenue, etc.) are identified in the data sources 106” (para. [0022]). Determining the data that is associated with ESG-related metrics by determining the taxonomy of product groups and associated key impact drivers associated with readily-accessible data, in Bessenyei, reads on the recited limitations.
“... generate, based on the determined local correlations and for each organization avatar entity (OAE) of a plurality of OAEs, a local correlation graph of associated ESG dimensions; ...” - See the aspects of Bessenyei and Li that have been cited above. Generating, based on the taxonomy and for the company, in Bessenyei, reads on the recited “generate, based on the determined local correlations and for each organization avatar entity (OAE) of a plurality of OAEs, ... associated ESG dimensions” limitation. Bessenyei does not appear to disclose generating specific graphs. The generating of the knowledge graph and the key information graphs, in Li, reads on the recited “a local correlation graph” limitation.
“... determine, based on the local correlation graph of associated ESG dimensions, global correlations between the ESG dimensions by determining mean correlation between specified ESG dimensions across the plurality of OAEs; and ...” - See the aspects of Bessenyei and Li that have been cited above. Determining, in association with the ESG-related metrics, taxonomies of products groups and key impact drivers for multiple companies, in Bessenyei, reads on the recited “determine, based on the ... associated ESG dimensions, global correlations between the ESG dimensions by determining ... correlation between specified ESG dimensions across the plurality of OAEs” limitation. Bessenyei does not appear to disclose generating specific graphs. The knowledge graphs and key information graphs, in Li, read on the recited “local correlation graph” limitation. Neither Bessenyei nor Li appears to disclose the use of mathematical means to determine correlations. Berg discloses “mean and median ESG ratings” (English-language translation, p. 1320), “Descriptive statistics of the aggregate ratings (ESG level) in 2014 for the six rating agencies” including “Mean” values for each “sample” (English-language translation, p. 1321). The use of means values in making determinations about relationships of EST ratings, in Berg, reads on the recited “mean” limitation.
“... generate, based on the determined global correlations and for each OAE of the plurality of OAEs, a global correlation graph of associated ESG dimensions; ...” - See the aspects of Bessenyei and Li that have been cited above. Generating, based on the combinations of taxonomies associated with the multiple companies, associated ESG data, and metrics, as in Bessenyei, reads on the recited “generate, based on the determined global correlations and for each OAE of the plurality of OAEs, ... associated ESG dimensions” limitation. Bessenyei does not appear to disclose generating specific graphs. The generating of the knowledge graphs and the key information graphs, in Li, for the multitude of companies in Bessenyei, reads on the recited “a global correlation graph” limitation.
“... an ESG dimension analyzer, executed by the at least one hardware processor, to: identify, based on the global correlation graph, correlated ESG dimensions for each ESG data analyzer of a plurality of ESG data analyzers with respect to a set of ESG dimensions on which an ESG data analyzer of the plurality of ESG data analyzers collects data for at least one OAE of the plurality of OAEs; ...” - See the aspects of Bessenyei that have been cited above. Bessenyei also discloses, “A Software-as-a-Service infrastructure is provided to perform data analytics, wherein a given analytic is defined in a spreadsheet according to a formula, and wherein the formula is associated with an Environmental, Social and Governance (ESG) impact” (para. [0009]). Elements of the SaaS infrastructure, executed by the hardware processor, to identify ESG impact of ESG data collected from the data sources, in Bessenyei, reads on the recited “an ESG dimension analyzer, executed by the at least one hardware processor, to: identify” limitation. Bessenyei does not appear to disclose generating specific graphs. Use of the knowledge graphs and the key information graphs, in Li, for the multitude of companies in Bessenyei, reads on the recited “based on the global correlation graph” limitation. Neither Bessenyei nor Li appears to disclose details about ESG data analyzers. Berg discloses, “Environmental, social, and governance (ESG) rating providers have become influential institutions” and “ESG ratings to obtain a third-party assessment of corporations’ ESG performance” (p. 1316), “Moody’s ESG, S&P Global, MSCI, and Sustainalytics have three dimensions (E, S, and G), Refinitiv has four, and KLD has seven. Below these first-level dimensions, there are between one and three levels of more granular sub-categories, depending on the rater” and “We impose our own taxonomy on the data to perform a meaningful comparison of these different rating systems” (p. 1323), and “rating agencies” (p. 1341). The taxonomy on ESG data for different rating systems of different rating agencies, wherein the ESG data is associated with corporations, in Berg, when applied to the ESG data and ESG metrics analyses, of Bessenyei, reads on the recited “correlated ESG dimensions for each ESG data analyzer of a plurality of ESG data analyzers with respect to a set of ESG dimensions on which an ESG data analyzer of the plurality of ESG data analyzers collects data for at least one OAE of the plurality of OAEs” limitation.
“... a collaboration analyzer, executed by the at least one hardware processor, to: determine, based on the correlated ESG dimensions for each ESG data analyzer of the plurality of ESG data analyzers, collaboration potential between the plurality of ESG data analyzers; ...” - See the aspects of Bessenyei and Berg that have been cited above. Berg also discloses, “to include several ESG ratings in the analysis” (p. 1341), and “use one particular ESG rating to measure a specific company characteristic” ( p. 1342). Analyzing the potential advantages of including several ESG ratings of multiple ESG rating agencies, where each rating agency of the pool could be used for one specific company characteristic, in Berg, when applied using the hardware processors, of Bessenyei, reads on the recited limitation.
“... a collaboration potential analyzer, executed by the at least one hardware processor, to: generate, based on the collaboration potential between the plurality of ESG data analyzers, decentralized groups of collaborating ESG data analyzers; ...” - See the aspects of Bessenyei and Berg that have been cited above. Berg also discloses, “dealing with the divergence of ESG ratings” (p. 1341). Considering potential for combining ratings from multiple ESG rating agencies to address problems of weight and scope divergence, resulting in the use of ratings of groups of ESG rating agencies, in Berg, when applied using the hardware processors, of Bessenyei, reads on the recited limitation.
“... a data model generator, executed by the at least one hardware processor, to: update, for each decentralized group of the decentralized groups of collaborating ESG data analyzers, a data model for each ESG data analyzer with respect to each ESG dimension corresponding to each OAE for which the ESG data analyzer is collecting data; ...” - See the aspects of Bessenyei and Berg that have been cited above. Bessenyei also discloses, “there may be one or more different types of impact frameworks (models), and these frameworks may be based on “operational” impacts, or “non-operational” impacts. Example of operational impacts with respect to a product may include, without limitation, data related to the product's manufacture, such as greenhouse gas (GHG) emissions, water consumption, air pollution, waste, employment-related factors (wage quality, diversity, equal opportunity, unemployment, location impact, etc.) and the like. Non-operational impacts typically include social and environmental metrics, and product impact (customer welfare, product quality and safety, accessibility, and the like). These characterizations are not intended to be limiting. During processing, these frameworks (and thus the data therein) may be combined or processed independently. For example, and in one embodiment, product, employment and environmental impact frameworks are combined in a unified model, and analytics derived therefrom” (para. [0024]), “the data supplied from the data sources 106 (possibly on a framework-specific basis) is supplied to the preprocessing stage 110, which performs various operations such as restructuring the data, culling unnecessary data, transforming data where necessary, filtering data, and combining data. The imputation stage 112 receives the output(s) from the preprocessing stage 110 and operates to fill-in data that is not present in the one or more data sources directly but may still be needed for computations. The populator stage 114 receives the data set(s) output from the imputation stage, and it provides for a data enrichment function that reorders the data sets to facilitate their use in association within the context of a network-accessible spreadsheet application” (para. [0025]), “The flowchart in FIG. 2 represents an operation of the aggregator stage 116. In general, this stage configures the computation engine, executes the computation(s) in a spreadsheet, aggregates data resulting from such computation(s), and generates the output(s) that are then returned to the requesting end user” (para. [0026]), and “calculations are run infrequently, e.g., when new data comes into the system, when a formula is changed” (para. [0029]). The generating of the impact framework models, by the hardware processors, including updating the models based on new data and calculations run thereon, in Bessenyei, reads on the recited “a data model generator, executed by the at least one hardware processor, to: update ... a data model ... with respect to each ESG dimension corresponding to each OAE for which ... is collecting data” limitation. The use of multiple ESG ratings agencies in combination with each other, wherein the agencies collect and analyze their specific ESG data and metrics about one or more companies, in Berg, reads on the recited “for each decentralized group of the decentralized groups of collaborating ESG data analyzers” and “for each ESG data analyzer with respect to each ESG dimension corresponding to each OAE for which the ESG data analyzer is collecting data” limitation.
“... an anomalous event analyzer, executed by the at least one hardware processor, to: identify, for the ESG data analyzer that is collecting data and based on an associated updated data model, a potential anomalous ESG event at a specific ESG dimension; and ...” - See the aspects of Bessenyei and Li that have been cited above. Use of the hardware processors to update the impact framework models, in Bessenyei, reads on the recited “executed by the at least one hardware processor” and “based on an associated updated data model” limitations. While Bessenyei discloses accessing data sources that have data about companies, Bessenyei does not appear to disclose that the data is of events. Li discloses, “to extract real-time basic ESG events from the obtained alternative data” (English-language translation, p. 8). Extracting and using data about ESG events, related to ESG data and metrics, in Li, reads on the recited “an anomalous event analyzer ... to: identify ... a potential anomalous ESG event at a specific ESG dimension” limitation. The ESG ratings agencies, in Berg, read on the recited “for the ESG data analyzer” limitation.
“... a OAE controller, executed by the at least one hardware processor, to: control, based on the identified potential anomalous ESG event, operation of the OAE associated with the ESG data analyzer that is collecting data.” - See the aspects of Bessenyei, Li, and Berg that have been cited above. Bessenyei also discloses, “The platform enables user (e.g., investors, managers, consumers, employees and other stakeholders) to understand ESG risks and opportunities, and also to quantify holistic impact of investments to maximize positive impact on the environment, and on society” (para. [0021]). The hardware processors used directly or indirectly by users, including managers of companies, to understand ESG risks and opportunities, in Bessenyei, wherein the risk and opportunities are associated with ESG events, of Li, and involve the ESG ratings agencies, of Berg, reads on the recited limitation. Companies taking actions to improve their ESG scores, in Berg (p. 1343), is an example of “operation of the OAE.”
Regarding Li, the reference discloses “ESG indicator monitoring” (English-language translation, p. 1), similar to the claimed invention and to Bessenyei. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the taxonomies and similar relationships between data, in Bessenyei, to include use of knowledge graphs and key information graphs, and consideration of ESG events, in Li, as such arrangements can indicate or represent influence and restriction relationships, per Li (English-language translation, p. 4).
Regarding Berg, the reference discloses “”ESG information” including “ESG ratings” (p. 1316), similar to the claimed invention and to Li and Bessenyei. It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the data, in Li, to include consideration of mean values of ESG metrics, and use of ESG ratings systems of multiple ESG ratings agencies, as in Berg, for statistical or mathematical advantages, and for the descriptiveness of such statistics, and for dealing with divergence of ratings, per Berg (pp. 1320 and 1321, and p. 1341).
Regarding claim 2, Bessenyei/Li/Berg teaches the following limitations:
“The apparatus according to claim 1, wherein the collaboration analyzer is executed by the at least one hardware processor to determine, based on the correlated ESG dimensions for each ESG data analyzer of the plurality of ESG data analyzers, the collaboration potential between the plurality of ESG data analyzers by: determining, for each pair of ESG data analyzers of the plurality of ESG data analyzers, a degree to which ESG data analyzers of the pair of ESG data analyzers collaborate with each other to enrich their data collection.” - See the aspects of Bessenyei and Berg that have been cited above. Using the hardware processor, of Bessenyei, to determine, based on particular ESG ratings from specific ESG ratings agencies, whether to use several ESG ratings, to deal with divergence, by avoiding problems of weight and scope divergence, in Berg, reads on the recited limitation. The rationales for combining the teachings of the cited references, from the rejection of claim 1, also apply to this rejection of claim 2.
Regarding claim 3, Bessenyei/Li/Berg teaches the following limitations:
“The apparatus according to claim 1, wherein the collaboration potential analyzer is executed by the at least one hardware processor to generate, based on the collaboration potential between the plurality of ESG data analyzers, the decentralized groups of collaborating ESG data analyzers by: generating a collaboration graph between the plurality of ESG data analyzers, wherein, for the collaboration graph, weights of edges represent collaboration potentials between connected ESG data analyzers.” - See the aspects of Bessenyei, Li, and Berg that have been cited above. Li also discloses, “key information includes: key nodes and key relationships” (English-language translation, p. 4.) Using the hardware processors, in Bessenyei, to generate, based on the potential for using ESG ratings from ESG ratings agencies, groupings of ESG ratings from ESG rating agencies, in Berg, and knowledge graphs and key information graphs of multiple ESG indicator monitoring entities, including key information in the form of key nodes and key relationships, as in Li, reads on the recited limitation. The rationales for combining the teachings of the cited references, from the rejection of claim 1, also apply to this rejection of claim 3.
Regarding claim 4, Bessenyei/Li/Berg teaches the following limitations:
“The apparatus according to claim 3, wherein the collaboration potential analyzer is executed by the at least one hardware processor to generate the collaboration graph between the plurality of ESG data analyzers by: retaining, for the collaboration graph, edges that include a collaboration potential that is greater than a specified threshold.” - See the aspects of Bessenyei, Li, and Berg that have been cited above. Li also discloses, “According to the alternative data of the listed company in the real-time monitoring, the continuous development of related public opinion information and other alternative data, the upstream supplier enterprises and downstream purchaser enterprise nodes and influence control relationships in the key information map are continuously updated and maintained” (English-language translation, p. 5).Using the hardware processors, in Bessenyei, to generate the key nodes and key relationships, and to either maintain them or update them based on the significance of changes in values of real-time monitored data, in Li, for the ESG ratings agencies that may be used in combination, per Berg, reads on the recited limitation.
Regarding claim 5, Bessenyei/Li/Berg teaches the following limitations:
“The apparatus according to claim 1, wherein the anomalous event analyzer is executed by the at least one hardware processor to identify, for the ESG data analyzer that is collecting data and based on the associated updated data model, the potential anomalous ESG event at the specific ESG dimension by: rebuilding, for the ESG data analyzer that is collecting data, a local data model to generate the updated data model; and determining a difference between the updated data model from the local data model.” - See the aspects of Bessenyei, Li, and Berg that have been cited above. The hardware processors being used to identify, impact framework models, and updating them based on real-time computations based on received data, resulting in differences between impact framework models at different points in time, and wherein the data is associated with ESG events, as in Li, and the data is monitored by the ESG ratings agencies, in Berg, reads on the recited limitation. The rationales for combining the teachings of the cited references, from the rejection of claim 1, also apply to this rejection of claim 5.
Regarding claim 6, Bessenyei/Li/Berg teaches the following limitations:
“The apparatus according to claim 1, wherein the anomalous event analyzer is executed by the at least one hardware processor to: determine, for a specified number of ESG dimensions, whether a data anomaly is true for the OAE associated with the ESG data analyzer; and identify, based on a determination that the data anomaly is true for the OAE associated with the ESG data analyzer for the specified number of ESG dimensions, the potential anomalous ESG event.” - See the aspects of Bessenyei, Li, and Berg that have been cited above. The hardware processors being used to determine, for ESG data and metrics, in Bessenyei, whether ESG events have occurred, in Li, wherein the ones that have occurred are received by the EST ratings agencies, of Berg, reads on the recited limitation. Any ESG data and metrics associated with a specific event is a data anomaly that is true, and is potentially anomalous in terms of uniqueness. The rationales for combining the teachings of the cited references, from the rejection of claim 1, also apply to this rejection of claim 6.
Regarding claim 7, Bessenyei/Li/Berg teaches the following limitations:
“The apparatus according to claim 1, wherein the anomalous event analyzer is executed by the at least one hardware processor to: determine, for a specified number of OAEs in a cluster of OAEs, whether a data anomaly is true; and identify, based on a determination that the data anomaly is true for the specified number of OAEs in the cluster of OAEs, the potential anomalous ESG event for the cluster of OAEs.” - See the aspects of Bessenyei and Li that have been cited above. The hardware processors determining, for multiple companies, actual ESG data and metrics for the companies, and associating with the ESG data and metrics the ESG events, of Li, reads on the recited limitation. The rationales for combining the teachings of the cited references, from the rejection of claim 1, also apply to this rejection of claim 7.
Regarding claim 8, Bessenyei/Li/Berg teaches the following limitations:
“The apparatus according to claim 1, wherein the anomalous event analyzer is executed by the at least one hardware processor to: determine, for a specified number of OAEs in a cluster of OAEs, whether a data anomaly is true for a specified ESG dimension; and identify, based on a determination that the data anomaly is true for the specified ESG dimension for the specified number of OAEs in the cluster of OAEs, the potential anomalous ESG event at a global level for the specified ESG dimension.” - See the aspects of Bessenyei and Li that have been cited above. The hardware processors being used to determine, for specific companies, whether ESG data and metrics apply, and identifying, based on association of the ESG data and metrics with the specific companies, in Bessenyei, the occurrence of ESG events, as in Li, reads on the recited limitation. The rationales for combining the teachings of the cited references, from the rejection of claim 1, also apply to this rejection of claim 8.
Regarding claims 9-13, while the claims are of different scope relative to claims 1, 3, and 4, the claims recite limitations similar to those recited by claims 1, 3, and 4. As such, the rationales applied in the rejection of claims 1, 3, and 4 also apply for purposes of rejecting claims 9-13. Claims 9-13 are, therefore, also rejected under 35 USC 103 as obvious in view of Bessenyei/Li/Berg.
Regarding claims 14-20, while the claims are of different scope relative to claims 1 and 5-9, the claims recite limitations similar to those recited by claims 1 and 5-9. As such, the rationales applied to reject claims 1 and 5-9 also apply for purposes of rejecting claims 14-20. Limitations recited by claims 14-20 that do not appear to have a counterpart in claims 1 and 5-9, such as the hardware limitations at the start of claim 14, are disclosed by the cited references (see, e.g., claim 1 of Bessenyei). Claims 14-20 are, therefore, also rejected under 35 USC 103 as obvious in view of Bessenyei/Li/Berg.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Such prior art includes the following:
U.S. Pat. No. 12,135,751 B1 to Wright et al. discloses, “scraping published internet data from one or more data sources based on or more environmental parameters, one or more social parameters, or one or more governance parameters. The method may also include receiving user data from an account database, a user device, or both; identifying correlations between the user data and the published internet data; wherein the correlations are associated with environmental parameters, social parameters, or governance parameters. The method may also include generating a dynamic interest report based on the correlations, wherein the dynamic interest report comprises recommendations of one or more events, one or more content items, or both associated with environmental, social, and governance (ESG) compliance. The method may then involve sending the dynamic interest report to the user device.” (Abstract.)
U.S. Pat. App. Pub. No. 2019/0236721 A1 to Polizzotto discloses, “receiving a social platform inquiry from a client; analyzing a current responsibility score associated with the client; defining one or more score conditions of the client based, at least in part, upon the current responsibility score; and recommending one or more social platforms based, at least in part, upon the one or more score conditions” (Abstract)
U.S. Pat. App. Pub. No. 2022/0414560 A1 to Abele et al. discloses, “An Environmental, Social, and Governance (ESG) rating is provided for an entity with multiple assets. A plurality of metrics are defined. A value for each metric for each asset is obtained if available, or is set to 0. Similarly, a value for each metric for a benchmark is obtained if available, or is set to 0. For each metric and for each asset, a weight is calculated as a difference between the corresponding value of such metric for the asset and the corresponding value of such metric for the benchmark. For each metric, the weights thereof are combined to produce a composite weight across all assets. For each composite weight, a point value is assigned thereto based on a corresponding risk model. The point values are aggregated, and the aggregate is adjusted based on a perceived risk for the entity to produce the ESG rating.” (Abstract.)
U.S. Pat. App. Pub. No. 2024/0192993 A1 to Misra et al. discloses “allocating computation resources using ESG reporting. In one aspect a method includes obtaining data from a knowledge source for an entity, the knowledge source comprising a plurality of ESG disclosures that relate to one or more ESG dimensions; computing vulnerability indicator scores that represent measures of latent vulnerability with respect to the ESG dimensions; computing descriptive distribution scores that represent distributions of descriptions of the ESG dimensions within the knowledge source; determining, using the vulnerability indicator scores and the descriptive distribution scores, an allocation of computational resources to ESG computational processes associated with the ESG dimensions that achieves an increased gain in sustainability for the entity; and initiating allocation of the computational resources to the ESG computational processes according to the determined allocation.” (Abstract.)
WIPO Int’l Pub. No. 2022/016093 A1 to Jemiri discloses, “a collaborative, multi-user platform can utilize a collaborative diversity resources planning and supply chain localization platform powered by artificial intelligent (Al). The collaborative, multi-user platform can utilize artificial intelligent (Al) and machine learning (ML) model to generate predictions and recommendations for users that can guide the users through, for example, bid generation for projects.” (Abstract.)
WIPO Int’l Pub. No. 2023/033362 A1 to Rim et al. discloses, “providing a metaverse service. According to the present invention, a user activity pattern in a real space can be predicted on the basis of the attributes of a user's avatar character in a virtual space, the normative policy environment in a metaverse can be improved on the basis of the level of affection and the level of doctrinal practice between the user's avatar character and target avatar characters related to the user in the virtual space, and crime can be prevented in the metaverse and in the real space on the basis of safety fitness.” (Abstract.)
Alva, Harshith, and Subrahmanya Bhat. "Accenture–Understanding sustainable business strategies." International Journal of Case Studies in Business, IT and Education (IJCSBE) 2.1 (2018): 54-63.
Allam, Zaheer, et al. "The metaverse as a virtual form of smart cities: Opportunities and challenges for environmental, economic, and social sustainability in urban futures." Smart Cities 5.3 (2022): 771-801.
Anshari M, Syafrudin M, Fitriyani NL, Razzaq A. Ethical responsibility and sustainability (ERS) development in a metaverse business model. Sustainability. 2022 Nov 28;14(23):15805.
Moro-Visconti, Roberto. "ESG-compliant Metaverse Ecosystems: Clues for Impact Investing." 2022.
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/THOMAS YIH HO/Primary Examiner, Art Unit 3624
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